Project Summary Autism Spectrum Disorder (ASD) is a heritable neurodevelopmental disorder characterized by heterogeneous genetic etiology and behavioral symptomatology. While recent genetics research suggests that the cumulative effects of many single nucleotide polymorphisms (SNPs) on many genes confer increased risk of ASD, very little is known about how additive genetic risk impacts brain function and structure. My dissertation work leverages variation at the genetic, neural, and behavioral levels by investigating how cumulative genetic risk for ASD on a gene implicated in social behavior across species ? the oxytocin receptor gene (OXTR) ? impacts network-level activity and connectivity in brain regions critical for social reward processing, and how risk-allele dosage relates to individual differences in ASD symptomatology. Results of my first dissertation study have shown that OXTR risk-allele dosage modulates resting-state connectivity of the reward network in both youth with ASD and typically developing (TD) youth, although in different ways. In youth with ASD, greater OXTR risk is associated with reduced connectivity between nodes of the reward circuit and greater symptom severity. Conversely, in TD youth, greater OXTR risk-allele dosage is associated with greater connectivity between the reward network and frontal brain regions involved in mentalizing and this, in turn, is associated with better social functioning (Hernandez et al., 2016a). These findings in TD youth suggest a compensatory neural mechanism in the face of increased genetic risk for ASD, and raise further questions about the role of other genetic (i.e., epistatic) and environmental/experiential variables in steering development along typical vs. atypical trajectories. My ongoing dissertation study examines the moderating effects of diagnosis and gender on the relationship between OXTR risk-allele dosage and neural processing of social rewards using functional neuroimaging (fMRI). While I have gained strong expertise in connectivity-based neuroimaging, genetics, and the neurobiology of ASD, my last dissertation project will provide additional training in analysis of task-related fMRI data and assessment of changes in task-based functional connectivity (i.e., training in psychophysiological interaction analyses). My dissertation work thus far highlights how little is known about the mechanisms by which individuals with the same genetic risk for mental illness have different outcomes (i.e., affected vs. resilient). As a post-doctoral researcher, I aim to gain additional training in the assessment of environmental and experiential variables that affect neurodevelopmental outcomes, as well as in advanced data-analytic approaches to model multilevel interactions between gene-brain-environment measures (e.g., statistical analysis of longitudinal data, machine learning algorithms, imaging-genetics of genome-wide data) with the ultimate goal of combining these data to better predict risk for mental illness and inform interventions. These aims are in line with the NIH Blueprint for NIMH, NICHD, and NINDS, as they seek to elucidate the neurobiological mechanisms underlying complex human behavior and neurodevelopmental disorders.